Implementation. We enumerate all text spans with length \([2, K_t]\)  within the sentences in transcripts, where \(K_t=30\)  for truncating text spans. For text spans matching, we try two sentence encoding methods to extract sentence embeddings: (1) average pooling on Glove word embeddings of words in sentences and text spans [1]}; (2) extracting features using pre-trained BERTBASE model and sum up the features in the last four layers then average over words in sentences and text spans [2]}. Then, we normalize the embeddings and find the best matching text spans for each protocol phrase based on cosine similarity. We also provide the exact matching as a baseline, which finds the longest transcript text span matched by a text span in protocol phrase.
<TABLE>